Vehicle routing problems (VRPs), which can be found in numerous real-world
applications, have been an important research topic for several decades.
Recently, the neural combinatorial optimization (NCO) approach that leverages a
learning-based model to solve VRPs without manual algorithm design has gained
substantial attention. However, current NCO methods typically require building
one model for each routing problem, which significantly hinders their practical
application for real-world industry problems with diverse attributes. In this
work, we make the first attempt to tackle the crucial challenge of
cross-problem generalization. In particular, we formulate VRPs as different
combinations of a set of shared underlying attributes and solve them
simultaneously via a single model through attribute composition. In this way,
our proposed model can successfully solve VRPs with unseen attribute
combinations in a zero-shot generalization manner. Extensive experiments are
conducted on eleven VRP variants, benchmark datasets, and industry logistic
scenarios. The results show that the unified model demonstrates superior
performance in the eleven VRPs, reducing the average gap to around 5% from over
20% in the existing approach and achieving a significant performance boost on
benchmark datasets as well as a real-world logistics application.

该论文通过属性组合的方式，建立了一个统一模型，成功解决了车辆路径问题的不同组合情况，大幅提高了解决方案的效率并在物流应用中取得了显著的性能提升。